Overview

Dataset statistics

Number of variables20
Number of observations2644
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory413.2 KiB
Average record size in memory160.0 B

Variable types

Numeric20

Warnings

Life expectancy is highly correlated with Adult Mortality and 3 other fieldsHigh correlation
Adult Mortality is highly correlated with Life expectancy and 1 other fieldsHigh correlation
infant deaths is highly correlated with Measles and 2 other fieldsHigh correlation
percentage expenditure is highly correlated with GDPHigh correlation
Hepatitis B is highly correlated with DiphtheriaHigh correlation
Measles is highly correlated with infant deaths and 1 other fieldsHigh correlation
BMI is highly correlated with Life expectancy and 1 other fieldsHigh correlation
under-five deaths is highly correlated with infant deaths and 2 other fieldsHigh correlation
Polio is highly correlated with DiphtheriaHigh correlation
Diphtheria is highly correlated with Hepatitis B and 1 other fieldsHigh correlation
HIV/AIDS is highly correlated with Adult MortalityHigh correlation
GDP is highly correlated with percentage expenditureHigh correlation
Population is highly correlated with infant deaths and 1 other fieldsHigh correlation
thinness 10-19 years is highly correlated with thinness 5-9 yearsHigh correlation
thinness 5-9 years is highly correlated with thinness 10-19 yearsHigh correlation
Income composition of resources is highly correlated with Life expectancy and 1 other fieldsHigh correlation
Schooling is highly correlated with Life expectancy and 2 other fieldsHigh correlation
Life expectancy is highly correlated with Adult Mortality and 11 other fieldsHigh correlation
Adult Mortality is highly correlated with Life expectancy and 2 other fieldsHigh correlation
infant deaths is highly correlated with Life expectancy and 6 other fieldsHigh correlation
percentage expenditure is highly correlated with GDP and 2 other fieldsHigh correlation
Hepatitis B is highly correlated with Polio and 1 other fieldsHigh correlation
Measles is highly correlated with infant deaths and 1 other fieldsHigh correlation
BMI is highly correlated with Life expectancy and 7 other fieldsHigh correlation
under-five deaths is highly correlated with Life expectancy and 6 other fieldsHigh correlation
Polio is highly correlated with Life expectancy and 3 other fieldsHigh correlation
Diphtheria is highly correlated with Life expectancy and 4 other fieldsHigh correlation
HIV/AIDS is highly correlated with Life expectancy and 7 other fieldsHigh correlation
GDP is highly correlated with Life expectancy and 3 other fieldsHigh correlation
thinness 10-19 years is highly correlated with Life expectancy and 3 other fieldsHigh correlation
thinness 5-9 years is highly correlated with Life expectancy and 3 other fieldsHigh correlation
Income composition of resources is highly correlated with Life expectancy and 11 other fieldsHigh correlation
Schooling is highly correlated with Life expectancy and 8 other fieldsHigh correlation
Life expectancy is highly correlated with Adult Mortality and 3 other fieldsHigh correlation
Adult Mortality is highly correlated with Life expectancyHigh correlation
infant deaths is highly correlated with under-five deathsHigh correlation
percentage expenditure is highly correlated with GDPHigh correlation
Hepatitis B is highly correlated with Polio and 1 other fieldsHigh correlation
under-five deaths is highly correlated with infant deathsHigh correlation
Polio is highly correlated with Hepatitis B and 1 other fieldsHigh correlation
Diphtheria is highly correlated with Hepatitis B and 1 other fieldsHigh correlation
HIV/AIDS is highly correlated with Life expectancyHigh correlation
GDP is highly correlated with percentage expenditureHigh correlation
thinness 10-19 years is highly correlated with thinness 5-9 yearsHigh correlation
thinness 5-9 years is highly correlated with thinness 10-19 yearsHigh correlation
Income composition of resources is highly correlated with Life expectancy and 1 other fieldsHigh correlation
Schooling is highly correlated with Life expectancy and 1 other fieldsHigh correlation
Polio is highly correlated with BMI and 5 other fieldsHigh correlation
BMI is highly correlated with Polio and 9 other fieldsHigh correlation
HIV/AIDS is highly correlated with Adult MortalityHigh correlation
percentage expenditure is highly correlated with Schooling and 1 other fieldsHigh correlation
under-five deaths is highly correlated with Measles and 4 other fieldsHigh correlation
Measles is highly correlated with under-five deaths and 1 other fieldsHigh correlation
thinness 10-19 years is highly correlated with BMI and 8 other fieldsHigh correlation
Alcohol is highly correlated with BMI and 1 other fieldsHigh correlation
Total expenditure is highly correlated with BMI and 1 other fieldsHigh correlation
Diphtheria is highly correlated with Polio and 6 other fieldsHigh correlation
Life expectancy is highly correlated with Polio and 5 other fieldsHigh correlation
Schooling is highly correlated with Polio and 10 other fieldsHigh correlation
Adult Mortality is highly correlated with Polio and 7 other fieldsHigh correlation
infant deaths is highly correlated with under-five deaths and 4 other fieldsHigh correlation
Population is highly correlated with under-five deaths and 2 other fieldsHigh correlation
Hepatitis B is highly correlated with Polio and 2 other fieldsHigh correlation
GDP is highly correlated with percentage expenditure and 1 other fieldsHigh correlation
thinness 5-9 years is highly correlated with BMI and 4 other fieldsHigh correlation
Income composition of resources is highly correlated with BMI and 7 other fieldsHigh correlation
Unnamed: 0 is highly correlated with thinness 10-19 yearsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
infant deaths has 803 (30.4%) zeros Zeros
Alcohol has 177 (6.7%) zeros Zeros
percentage expenditure has 498 (18.8%) zeros Zeros
Hepatitis B has 519 (19.6%) zeros Zeros
Measles has 899 (34.0%) zeros Zeros
BMI has 34 (1.3%) zeros Zeros
under-five deaths has 747 (28.3%) zeros Zeros
Total expenditure has 208 (7.9%) zeros Zeros
GDP has 352 (13.3%) zeros Zeros
Population has 540 (20.4%) zeros Zeros
thinness 10-19 years has 34 (1.3%) zeros Zeros
thinness 5-9 years has 34 (1.3%) zeros Zeros
Income composition of resources has 230 (8.7%) zeros Zeros
Schooling has 141 (5.3%) zeros Zeros

Reproduction

Analysis started2022-03-15 16:12:39.088649
Analysis finished2022-03-15 16:13:22.022696
Duration42.93 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2644
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1321.5
Minimum0
Maximum2643
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:22.114023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile132.15
Q1660.75
median1321.5
Q31982.25
95-th percentile2510.85
Maximum2643
Range2643
Interquartile range (IQR)1321.5

Descriptive statistics

Standard deviation763.4013798
Coefficient of variation (CV)0.5776779264
Kurtosis-1.2
Mean1321.5
Median Absolute Deviation (MAD)661
Skewness0
Sum3494046
Variance582781.6667
MonotonicityStrictly increasing
2022-03-15T11:13:22.238881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
11761
 
< 0.1%
11721
 
< 0.1%
11701
 
< 0.1%
11681
 
< 0.1%
11661
 
< 0.1%
11641
 
< 0.1%
11621
 
< 0.1%
11601
 
< 0.1%
11581
 
< 0.1%
Other values (2634)2634
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
26431
< 0.1%
26421
< 0.1%
26411
< 0.1%
26401
< 0.1%
26391
< 0.1%
26381
< 0.1%
26371
< 0.1%
26361
< 0.1%
26351
< 0.1%
26341
< 0.1%

Life expectancy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct359
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.06879728
Minimum0
Maximum89
Zeros9
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:22.356190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51.4
Q162.975
median72.1
Q375.8
95-th percentile82
Maximum89
Range89
Interquartile range (IQR)12.825

Descriptive statistics

Standard deviation10.35175726
Coefficient of variation (CV)0.1498760319
Kurtosis5.676098829
Mean69.06879728
Median Absolute Deviation (MAD)5.9
Skewness-1.429161629
Sum182617.9
Variance107.1588785
MonotonicityNot monotonic
2022-03-15T11:13:22.465209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7344
 
1.7%
7530
 
1.1%
7826
 
1.0%
73.624
 
0.9%
8123
 
0.9%
73.923
 
0.9%
74.122
 
0.8%
7622
 
0.8%
74.522
 
0.8%
73.521
 
0.8%
Other values (349)2387
90.3%
ValueCountFrequency (%)
09
0.3%
36.31
 
< 0.1%
391
 
< 0.1%
411
 
< 0.1%
41.51
 
< 0.1%
42.31
 
< 0.1%
43.11
 
< 0.1%
43.31
 
< 0.1%
43.51
 
< 0.1%
441
 
< 0.1%
ValueCountFrequency (%)
8911
0.4%
8810
0.4%
878
0.3%
8613
0.5%
8512
0.5%
8411
0.4%
83.71
 
< 0.1%
83.52
 
0.1%
83.41
 
< 0.1%
83.31
 
< 0.1%

Adult Mortality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct409
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.4652042
Minimum0
Maximum699
Zeros9
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:22.583287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q173
median143
Q3226.25
95-th percentile392.7
Maximum699
Range699
Interquartile range (IQR)153.25

Descriptive statistics

Standard deviation121.1869533
Coefficient of variation (CV)0.7459255896
Kurtosis1.407400036
Mean162.4652042
Median Absolute Deviation (MAD)76
Skewness1.082963128
Sum429558
Variance14686.27764
MonotonicityNot monotonic
2022-03-15T11:13:23.057189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1228
 
1.1%
1426
 
1.0%
1625
 
0.9%
1122
 
0.8%
14422
 
0.8%
13822
 
0.8%
1920
 
0.8%
1519
 
0.7%
12719
 
0.7%
6619
 
0.7%
Other values (399)2422
91.6%
ValueCountFrequency (%)
09
0.3%
111
0.4%
28
0.3%
36
 
0.2%
44
 
0.2%
52
 
0.1%
613
0.5%
715
0.6%
810
0.4%
912
0.5%
ValueCountFrequency (%)
6991
< 0.1%
6931
< 0.1%
6821
< 0.1%
6791
< 0.1%
6751
< 0.1%
6661
< 0.1%
6541
< 0.1%
6521
< 0.1%
6481
< 0.1%
6471
< 0.1%

infant deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct197
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.14409985
Minimum0
Maximum1800
Zeros803
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:23.181077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q318
95-th percentile95
Maximum1800
Range1800
Interquartile range (IQR)18

Descriptive statistics

Standard deviation123.9323197
Coefficient of variation (CV)3.979319367
Kurtosis105.2156075
Mean31.14409985
Median Absolute Deviation (MAD)2
Skewness9.349131179
Sum82345
Variance15359.21986
MonotonicityNot monotonic
2022-03-15T11:13:23.289354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0803
30.4%
1317
 
12.0%
2203
 
7.7%
3157
 
5.9%
479
 
3.0%
857
 
2.2%
1044
 
1.7%
744
 
1.7%
937
 
1.4%
1136
 
1.4%
Other values (187)867
32.8%
ValueCountFrequency (%)
0803
30.4%
1317
 
12.0%
2203
 
7.7%
3157
 
5.9%
479
 
3.0%
535
 
1.3%
635
 
1.3%
744
 
1.7%
857
 
2.2%
937
 
1.4%
ValueCountFrequency (%)
18002
0.1%
17002
0.1%
16001
< 0.1%
15002
0.1%
14001
< 0.1%
13002
0.1%
12001
< 0.1%
11002
0.1%
10001
< 0.1%
9571
< 0.1%

Alcohol
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1032
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.328683812
Minimum0
Maximum17.87
Zeros177
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:23.402410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4075
median3.225
Q37.495
95-th percentile12
Maximum17.87
Range17.87
Interquartile range (IQR)7.0875

Descriptive statistics

Standard deviation4.146851238
Coefficient of variation (CV)0.9579935651
Kurtosis-0.7594094965
Mean4.328683812
Median Absolute Deviation (MAD)3.17
Skewness0.660487079
Sum11445.04
Variance17.19637519
MonotonicityNot monotonic
2022-03-15T11:13:23.512879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01275
 
10.4%
0177
 
6.7%
0.0314
 
0.5%
0.0212
 
0.5%
0.0912
 
0.5%
0.2110
 
0.4%
1.189
 
0.3%
0.559
 
0.3%
0.569
 
0.3%
0.549
 
0.3%
Other values (1022)2108
79.7%
ValueCountFrequency (%)
0177
6.7%
0.01275
10.4%
0.0212
 
0.5%
0.0314
 
0.5%
0.046
 
0.2%
0.058
 
0.3%
0.068
 
0.3%
0.072
 
0.1%
0.088
 
0.3%
0.0912
 
0.5%
ValueCountFrequency (%)
17.871
< 0.1%
17.311
< 0.1%
16.991
< 0.1%
16.581
< 0.1%
16.351
< 0.1%
15.521
< 0.1%
15.191
< 0.1%
15.141
< 0.1%
15.071
< 0.1%
15.042
0.1%

percentage expenditure
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2147
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean792.4655522
Minimum0
Maximum19479.91161
Zeros498
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:23.637989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.044564582
median70.64878737
Q3485.9595594
95-th percentile5066.826671
Maximum19479.91161
Range19479.91161
Interquartile range (IQR)478.9149948

Descriptive statistics

Standard deviation2077.768681
Coefficient of variation (CV)2.6219041
Kurtosis24.06125676
Mean792.4655522
Median Absolute Deviation (MAD)70.64878737
Skewness4.444776518
Sum2095278.92
Variance4317122.69
MonotonicityNot monotonic
2022-03-15T11:13:23.758924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0498
 
18.8%
151.10455521
 
< 0.1%
0.9624970521
 
< 0.1%
6164.4554021
 
< 0.1%
70.271131791
 
< 0.1%
5.1032494381
 
< 0.1%
8.7582145381
 
< 0.1%
3.433343641
 
< 0.1%
2698.018171
 
< 0.1%
2160.3801991
 
< 0.1%
Other values (2137)2137
80.8%
ValueCountFrequency (%)
0498
18.8%
0.099872191
 
< 0.1%
0.1080559731
 
< 0.1%
0.275648261
 
< 0.1%
0.3284180561
 
< 0.1%
0.3882537721
 
< 0.1%
0.3972287641
 
< 0.1%
0.53057281
 
< 0.1%
0.6615403711
 
< 0.1%
0.667515051
 
< 0.1%
ValueCountFrequency (%)
19479.911611
< 0.1%
19099.045061
< 0.1%
18961.34861
< 0.1%
18822.867321
< 0.1%
18379.329741
< 0.1%
17028.527981
< 0.1%
16255.161981
< 0.1%
15515.752341
< 0.1%
15345.49071
< 0.1%
15268.064451
< 0.1%

Hepatitis B
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct87
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.53290469
Minimum0
Maximum99
Zeros519
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:23.880641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.75
median87
Q396
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)81.25

Descriptive statistics

Standard deviation39.22134736
Coefficient of variation (CV)0.5984985337
Kurtosis-1.018651157
Mean65.53290469
Median Absolute Deviation (MAD)11
Skewness-0.8708038193
Sum173269
Variance1538.314089
MonotonicityNot monotonic
2022-03-15T11:13:23.991640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0519
19.6%
99223
 
8.4%
98195
 
7.4%
96155
 
5.9%
97141
 
5.3%
95134
 
5.1%
94111
 
4.2%
9394
 
3.6%
9269
 
2.6%
8967
 
2.5%
Other values (77)936
35.4%
ValueCountFrequency (%)
0519
19.6%
11
 
< 0.1%
24
 
0.2%
43
 
0.1%
57
 
0.3%
615
 
0.6%
717
 
0.6%
836
 
1.4%
951
 
1.9%
111
 
< 0.1%
ValueCountFrequency (%)
99223
8.4%
98195
7.4%
97141
5.3%
96155
5.9%
95134
5.1%
94111
4.2%
9394
3.6%
9269
 
2.6%
9167
 
2.5%
8967
 
2.5%

Measles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct859
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2432.922844
Minimum0
Maximum212183
Zeros899
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:24.108387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14
Q3329.75
95-th percentile9561.55
Maximum212183
Range212183
Interquartile range (IQR)329.75

Descriptive statistics

Standard deviation11868.65244
Coefficient of variation (CV)4.878351349
Kurtosis110.7863047
Mean2432.922844
Median Absolute Deviation (MAD)14
Skewness9.361772676
Sum6432648
Variance140864910.7
MonotonicityNot monotonic
2022-03-15T11:13:24.219276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0899
34.0%
198
 
3.7%
266
 
2.5%
341
 
1.6%
430
 
1.1%
629
 
1.1%
725
 
0.9%
525
 
0.9%
823
 
0.9%
1020
 
0.8%
Other values (849)1388
52.5%
ValueCountFrequency (%)
0899
34.0%
198
 
3.7%
266
 
2.5%
341
 
1.6%
430
 
1.1%
525
 
0.9%
629
 
1.1%
725
 
0.9%
823
 
0.9%
918
 
0.7%
ValueCountFrequency (%)
2121831
< 0.1%
1824851
< 0.1%
1681071
< 0.1%
1412581
< 0.1%
1338021
< 0.1%
1314411
< 0.1%
1242191
< 0.1%
1187121
< 0.1%
1109271
< 0.1%
1090231
< 0.1%

BMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct592
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.66274584
Minimum0
Maximum87.3
Zeros34
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:24.332684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.1
Q118.9
median43
Q355.9
95-th percentile64.5
Maximum87.3
Range87.3
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.33975045
Coefficient of variation (CV)0.5400495901
Kurtosis-1.291098471
Mean37.66274584
Median Absolute Deviation (MAD)16.7
Skewness-0.2047583888
Sum99580.3
Variance413.7054485
MonotonicityNot monotonic
2022-03-15T11:13:24.439803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
1.3%
58.516
 
0.6%
54.215
 
0.6%
59.914
 
0.5%
55.814
 
0.5%
5714
 
0.5%
52.813
 
0.5%
59.413
 
0.5%
58.113
 
0.5%
2.111
 
0.4%
Other values (582)2487
94.1%
ValueCountFrequency (%)
034
1.3%
1.41
 
< 0.1%
1.81
 
< 0.1%
21
 
< 0.1%
2.111
 
0.4%
2.27
 
0.3%
2.36
 
0.2%
2.45
 
0.2%
2.58
 
0.3%
2.64
 
0.2%
ValueCountFrequency (%)
87.31
< 0.1%
83.31
< 0.1%
82.81
< 0.1%
81.61
< 0.1%
77.61
< 0.1%
77.31
< 0.1%
77.11
< 0.1%
76.71
< 0.1%
76.21
< 0.1%
75.71
< 0.1%

under-five deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct240
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.16036309
Minimum0
Maximum2500
Zeros747
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:24.549198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q323
95-th percentile136
Maximum2500
Range2500
Interquartile range (IQR)23

Descriptive statistics

Standard deviation168.4859286
Coefficient of variation (CV)3.903718981
Kurtosis99.81352476
Mean43.16036309
Median Absolute Deviation (MAD)3
Skewness9.090007872
Sum114116
Variance28387.50814
MonotonicityNot monotonic
2022-03-15T11:13:24.664619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0747
28.3%
1329
 
12.4%
2163
 
6.2%
4141
 
5.3%
3121
 
4.6%
1250
 
1.9%
640
 
1.5%
1038
 
1.4%
938
 
1.4%
835
 
1.3%
Other values (230)942
35.6%
ValueCountFrequency (%)
0747
28.3%
1329
12.4%
2163
 
6.2%
3121
 
4.6%
4141
 
5.3%
535
 
1.3%
640
 
1.5%
727
 
1.0%
835
 
1.3%
938
 
1.4%
ValueCountFrequency (%)
25001
< 0.1%
24001
< 0.1%
23001
< 0.1%
22001
< 0.1%
21001
< 0.1%
20002
0.1%
19001
< 0.1%
18001
< 0.1%
17001
< 0.1%
16001
< 0.1%

Polio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct74
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.96709531
Minimum0
Maximum99
Zeros19
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:24.778306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)19

Descriptive statistics

Standard deviation24.52152905
Coefficient of variation (CV)0.2991630843
Kurtosis3.408574123
Mean81.96709531
Median Absolute Deviation (MAD)6
Skewness-2.045952733
Sum216721
Variance601.3053868
MonotonicityNot monotonic
2022-03-15T11:13:24.895095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99349
 
13.2%
98235
 
8.9%
97191
 
7.2%
96189
 
7.1%
95161
 
6.1%
94132
 
5.0%
93106
 
4.0%
9286
 
3.3%
9166
 
2.5%
8864
 
2.4%
Other values (64)1065
40.3%
ValueCountFrequency (%)
019
 
0.7%
37
 
0.3%
411
 
0.4%
58
 
0.3%
611
 
0.4%
721
 
0.8%
837
1.4%
960
2.3%
171
 
< 0.1%
231
 
< 0.1%
ValueCountFrequency (%)
99349
13.2%
98235
8.9%
97191
7.2%
96189
7.1%
95161
6.1%
94132
 
5.0%
93106
 
4.0%
9286
 
3.3%
9166
 
2.5%
8952
 
2.0%

Total expenditure
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct796
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.431077912
Minimum0
Maximum17.24
Zeros208
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:25.011011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.6775
median5.55
Q37.3725
95-th percentile9.6485
Maximum17.24
Range17.24
Interquartile range (IQR)3.695

Descriptive statistics

Standard deviation2.821981619
Coefficient of variation (CV)0.5195988099
Kurtosis-0.1416073664
Mean5.431077912
Median Absolute Deviation (MAD)1.85
Skewness-0.03049886366
Sum14359.77
Variance7.96358026
MonotonicityNot monotonic
2022-03-15T11:13:25.132452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0208
 
7.9%
4.614
 
0.5%
6.711
 
0.4%
3.410
 
0.4%
5.610
 
0.4%
5.829
 
0.3%
6.19
 
0.3%
5.259
 
0.3%
9.19
 
0.3%
4.248
 
0.3%
Other values (786)2347
88.8%
ValueCountFrequency (%)
0208
7.9%
0.371
 
< 0.1%
0.651
 
< 0.1%
0.741
 
< 0.1%
0.761
 
< 0.1%
0.921
 
< 0.1%
1.12
 
0.1%
1.123
 
0.1%
1.152
 
0.1%
1.172
 
0.1%
ValueCountFrequency (%)
17.241
< 0.1%
14.391
< 0.1%
13.831
< 0.1%
13.761
< 0.1%
13.731
< 0.1%
13.711
< 0.1%
13.661
< 0.1%
13.631
< 0.1%
13.441
< 0.1%
13.381
< 0.1%

Diphtheria
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct82
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.92662632
Minimum0
Maximum99
Zeros19
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:25.260573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q178
median93
Q397
95-th percentile99
Maximum99
Range99
Interquartile range (IQR)19

Descriptive statistics

Standard deviation24.50277201
Coefficient of variation (CV)0.2990819116
Kurtosis3.340316386
Mean81.92662632
Median Absolute Deviation (MAD)6
Skewness-2.041433798
Sum216614
Variance600.3858364
MonotonicityNot monotonic
2022-03-15T11:13:25.375621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99327
 
12.4%
98233
 
8.8%
97190
 
7.2%
96174
 
6.6%
95173
 
6.5%
94128
 
4.8%
93115
 
4.3%
9287
 
3.3%
9179
 
3.0%
8973
 
2.8%
Other values (72)1065
40.3%
ValueCountFrequency (%)
019
 
0.7%
21
 
< 0.1%
34
 
0.2%
412
 
0.5%
58
 
0.3%
616
 
0.6%
717
 
0.6%
837
1.4%
951
1.9%
161
 
< 0.1%
ValueCountFrequency (%)
99327
12.4%
98233
8.8%
97190
7.2%
96174
6.6%
95173
6.5%
94128
 
4.8%
93115
 
4.3%
9287
 
3.3%
9179
 
3.0%
8973
 
2.8%

HIV/AIDS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct177
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.61709531
Minimum0.1
Maximum50.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:25.482439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.1
median0.1
Q30.8
95-th percentile7.285
Maximum50.6
Range50.5
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation4.82215012
Coefficient of variation (CV)2.981982626
Kurtosis40.63260529
Mean1.61709531
Median Absolute Deviation (MAD)0
Skewness5.803790413
Sum4275.6
Variance23.25313178
MonotonicityNot monotonic
2022-03-15T11:13:25.600489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11600
60.5%
0.2106
 
4.0%
0.3105
 
4.0%
0.464
 
2.4%
0.540
 
1.5%
0.634
 
1.3%
0.830
 
1.1%
0.928
 
1.1%
0.726
 
1.0%
1.521
 
0.8%
Other values (167)590
 
22.3%
ValueCountFrequency (%)
0.11600
60.5%
0.2106
 
4.0%
0.3105
 
4.0%
0.464
 
2.4%
0.540
 
1.5%
0.634
 
1.3%
0.726
 
1.0%
0.830
 
1.1%
0.928
 
1.1%
110
 
0.4%
ValueCountFrequency (%)
50.61
< 0.1%
50.31
< 0.1%
49.91
< 0.1%
49.11
< 0.1%
48.81
< 0.1%
46.41
< 0.1%
43.71
< 0.1%
40.71
< 0.1%
40.21
< 0.1%
38.81
< 0.1%

GDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2293
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6726.080237
Minimum0
Maximum119172.7418
Zeros352
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:25.714131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1238.6268817
median1254.65281
Q35277.746332
95-th percentile38527.25734
Maximum119172.7418
Range119172.7418
Interquartile range (IQR)5039.119451

Descriptive statistics

Standard deviation13873.88146
Coefficient of variation (CV)2.062699369
Kurtosis13.782567
Mean6726.080237
Median Absolute Deviation (MAD)1254.65281
Skewness3.390310702
Sum17783756.15
Variance192484586.8
MonotonicityNot monotonic
2022-03-15T11:13:25.836912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0352
 
13.3%
166.2317851
 
< 0.1%
1473.319231
 
< 0.1%
697.663851
 
< 0.1%
6376.1831531
 
< 0.1%
47447.47621
 
< 0.1%
3821.89371
 
< 0.1%
4234.5544431
 
< 0.1%
21896.65271
 
< 0.1%
1276.2651
 
< 0.1%
Other values (2283)2283
86.3%
ValueCountFrequency (%)
0352
13.3%
1.681351
 
< 0.1%
3.6859491
 
< 0.1%
4.61357451
 
< 0.1%
5.66872641
 
< 0.1%
8.3764321
 
< 0.1%
11.1472771
 
< 0.1%
11.336781
 
< 0.1%
11.5531961
 
< 0.1%
11.6313771
 
< 0.1%
ValueCountFrequency (%)
119172.74181
< 0.1%
115761.5771
< 0.1%
114293.84331
< 0.1%
113751.851
< 0.1%
89739.71171
< 0.1%
88564.822981
< 0.1%
87998.444681
< 0.1%
87646.753461
< 0.1%
86852.71191
< 0.1%
85948.7461
< 0.1%

Population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2098
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10521518.87
Minimum0
Maximum1293859294
Zeros540
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:25.953691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112859
median622796.5
Q35194998.75
95-th percentile42182320.9
Maximum1293859294
Range1293859294
Interquartile range (IQR)5182139.75

Descriptive statistics

Standard deviation56809774.24
Coefficient of variation (CV)5.399389096
Kurtosis345.7775414
Mean10521518.87
Median Absolute Deviation (MAD)622796.5
Skewness17.1560811
Sum2.781889588 × 1010
Variance3.227350449 × 1015
MonotonicityNot monotonic
2022-03-15T11:13:26.075465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0540
 
20.4%
4444
 
0.2%
7182392
 
0.1%
11412
 
0.1%
1274452
 
0.1%
2922
 
0.1%
18882681
 
< 0.1%
23974361
 
< 0.1%
391454881
 
< 0.1%
26682891
 
< 0.1%
Other values (2088)2088
79.0%
ValueCountFrequency (%)
0540
20.4%
341
 
< 0.1%
361
 
< 0.1%
411
 
< 0.1%
431
 
< 0.1%
1231
 
< 0.1%
1351
 
< 0.1%
2861
 
< 0.1%
2922
 
0.1%
2971
 
< 0.1%
ValueCountFrequency (%)
12938592941
< 0.1%
11796812391
< 0.1%
11619777191
< 0.1%
11441186741
< 0.1%
11261357771
< 0.1%
2581621131
< 0.1%
2551311161
< 0.1%
2488832321
< 0.1%
2425241231
< 0.1%
2361592761
< 0.1%

thinness 10-19 years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct198
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.743910741
Minimum0
Maximum27.7
Zeros34
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:26.191166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.415
Q11.5
median3.2
Q37.2
95-th percentile13.3
Maximum27.7
Range27.7
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.467839082
Coefficient of variation (CV)0.9418050477
Kurtosis4.220610583
Mean4.743910741
Median Absolute Deviation (MAD)2.3
Skewness1.763873058
Sum12542.9
Variance19.96158606
MonotonicityNot monotonic
2022-03-15T11:13:26.298585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174
 
2.8%
1.965
 
2.5%
2.161
 
2.3%
1.261
 
2.3%
2.258
 
2.2%
257
 
2.2%
0.957
 
2.2%
1.153
 
2.0%
1.352
 
2.0%
0.851
 
1.9%
Other values (188)2055
77.7%
ValueCountFrequency (%)
034
1.3%
0.123
0.9%
0.239
1.5%
0.332
1.2%
0.45
 
0.2%
0.535
1.3%
0.641
1.6%
0.744
1.7%
0.851
1.9%
0.957
2.2%
ValueCountFrequency (%)
27.71
 
< 0.1%
27.51
 
< 0.1%
27.41
 
< 0.1%
27.31
 
< 0.1%
27.22
0.1%
27.12
0.1%
273
0.1%
26.92
0.1%
26.82
0.1%
26.71
 
< 0.1%

thinness 5-9 years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct204
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.778214826
Minimum0
Maximum28.6
Zeros34
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:26.415506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.5
median3.2
Q37.3
95-th percentile13.6
Maximum28.6
Range28.6
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.553417977
Coefficient of variation (CV)0.9529538003
Kurtosis4.632168812
Mean4.778214826
Median Absolute Deviation (MAD)2.3
Skewness1.831556234
Sum12633.6
Variance20.73361528
MonotonicityNot monotonic
2022-03-15T11:13:26.523865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.969
 
2.6%
1.167
 
2.5%
1.963
 
2.4%
162
 
2.3%
2.161
 
2.3%
1.358
 
2.2%
252
 
2.0%
2.549
 
1.9%
0.548
 
1.8%
1.747
 
1.8%
Other values (194)2068
78.2%
ValueCountFrequency (%)
034
1.3%
0.131
1.2%
0.245
1.7%
0.325
 
0.9%
0.417
 
0.6%
0.548
1.8%
0.638
1.4%
0.745
1.7%
0.836
1.4%
0.969
2.6%
ValueCountFrequency (%)
28.61
< 0.1%
28.51
< 0.1%
28.41
< 0.1%
28.31
< 0.1%
28.21
< 0.1%
28.11
< 0.1%
282
0.1%
27.91
< 0.1%
27.82
0.1%
27.71
< 0.1%

Income composition of resources
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct622
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6028789713
Minimum0
Maximum0.948
Zeros230
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:26.637195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.473
median0.668
Q30.781
95-th percentile0.89285
Maximum0.948
Range0.948
Interquartile range (IQR)0.308

Descriptive statistics

Standard deviation0.244039778
Coefficient of variation (CV)0.4047906623
Kurtosis0.7025925394
Mean0.6028789713
Median Absolute Deviation (MAD)0.145
Skewness-1.110901853
Sum1594.012
Variance0.05955541327
MonotonicityNot monotonic
2022-03-15T11:13:26.747157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0230
 
8.7%
0.715
 
0.6%
0.73912
 
0.5%
0.63612
 
0.5%
0.8611
 
0.4%
0.79711
 
0.4%
0.73511
 
0.4%
0.70311
 
0.4%
0.87711
 
0.4%
0.68611
 
0.4%
Other values (612)2309
87.3%
ValueCountFrequency (%)
0230
8.7%
0.2531
 
< 0.1%
0.2551
 
< 0.1%
0.2611
 
< 0.1%
0.2661
 
< 0.1%
0.2683
 
0.1%
0.271
 
< 0.1%
0.2761
 
< 0.1%
0.2781
 
< 0.1%
0.2791
 
< 0.1%
ValueCountFrequency (%)
0.9481
 
< 0.1%
0.9451
 
< 0.1%
0.9421
 
< 0.1%
0.9411
 
< 0.1%
0.9391
 
< 0.1%
0.9381
 
< 0.1%
0.9371
 
< 0.1%
0.9365
0.2%
0.9342
 
0.1%
0.9331
 
< 0.1%

Schooling
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct173
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.48267776
Minimum0
Maximum20.7
Zeros141
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2022-03-15T11:13:26.856461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.5
median12.3
Q314.2
95-th percentile16.9
Maximum20.7
Range20.7
Interquartile range (IQR)4.7

Descriptive statistics

Standard deviation4.170684252
Coefficient of variation (CV)0.3632153004
Kurtosis1.064846057
Mean11.48267776
Median Absolute Deviation (MAD)2.3
Skewness-0.9793273376
Sum30360.2
Variance17.39460713
MonotonicityNot monotonic
2022-03-15T11:13:26.969400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0141
 
5.3%
12.956
 
2.1%
13.344
 
1.7%
12.844
 
1.7%
12.541
 
1.6%
12.639
 
1.5%
12.338
 
1.4%
12.438
 
1.4%
15.838
 
1.4%
11.737
 
1.4%
Other values (163)2128
80.5%
ValueCountFrequency (%)
0141
5.3%
2.81
 
< 0.1%
2.94
 
0.2%
31
 
< 0.1%
3.11
 
< 0.1%
3.31
 
< 0.1%
3.41
 
< 0.1%
3.53
 
0.1%
3.61
 
< 0.1%
3.72
 
0.1%
ValueCountFrequency (%)
20.71
 
< 0.1%
20.61
 
< 0.1%
20.51
 
< 0.1%
20.43
0.1%
20.34
0.2%
20.12
0.1%
19.81
 
< 0.1%
19.71
 
< 0.1%
19.53
0.1%
19.32
0.1%

Interactions

2022-03-15T11:12:42.960029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.085723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.173614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.267222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.360742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.454977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.554548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.744079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.847218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:43.931556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.022082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.110815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.223349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.322569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.422164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.527704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.632699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.723300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.817194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:44.925421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.030517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.134945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.233136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.331676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.427862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.522095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.619906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.705727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.791330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.874091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:45.959521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.043926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.136288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.221073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.308467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.398505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.490009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.672191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.774341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.855824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:46.936955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.032792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.126081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.226935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.327732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.426545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.531372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.630913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.725037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.815949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:47.909612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.003146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.103938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.199237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.292182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.389058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.487417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.584244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.682790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.778449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.869147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:48.965973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.068809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.175970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.284506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.389747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.498585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.603889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.707488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.802219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.897055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:49.990774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:50.224047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:50.337573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:50.432715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:50.531344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:50.631617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:50.730414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:50.831833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:50.931584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.029721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.129909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.226756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.331533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.430928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.530689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.636135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.730180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.821810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:51.910432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.005452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.098212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.198271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.292416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.383565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.480046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.578667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.673149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.767619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.860303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:52.950065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.048601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.144107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.247415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.352413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.457296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.565986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.665561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.762545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.857933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:53.958584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:54.057689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:54.161166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:54.260502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:54.512422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:54.634381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:54.739531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:54.838811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:54.940100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.038534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.134239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.221161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.304241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.396676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.489177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.581412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.679720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.766946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.851730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:55.934965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.026875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.113607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.206882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.292845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.377967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.466791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.556259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.642054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.729716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.814895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.897387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:56.980334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.065004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.154362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.244152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.332241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.426178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.509325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.595105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.677555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.762650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.844546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:57.934108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.019448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.104051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.191538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.280578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.365375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.453829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.537054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.617301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.700383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.779935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.869387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:58.958377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.047986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.148935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.245214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.328326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.600559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.703903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.787376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.876640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:12:59.960570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:00.043534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:00.127614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:00.216567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:00.301994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:00.389383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2022-03-15T11:13:19.681349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:19.765420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:19.848351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:19.942539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.033291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.119722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.212524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.297425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.379899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.461103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.545832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.628824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.722621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.806706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.890628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:20.981136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:21.072431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:21.156793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:21.243048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-03-15T11:13:21.326219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-03-15T11:13:27.092722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-15T11:13:27.321909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-15T11:13:27.537323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-15T11:13:27.755167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-03-15T11:13:21.521745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-15T11:13:21.873038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0Life expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 10-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
0065.0263.0620.0171.27962465.0115419.1836.08.1665.00.1584.25921033736494.017.217.30.47910.1
1159.9271.0640.0173.52358262.049218.68658.08.1862.00.1612.696514327582.017.517.50.47610.0
2259.9268.0660.0173.21924364.043018.18962.08.1364.00.1631.74497631731688.017.717.70.4709.9
3359.5272.0690.0178.18421567.0278717.69367.08.5267.00.1669.9590003696958.017.918.00.4639.8
4459.2275.0710.017.09710968.0301317.29768.07.8768.00.163.5372312978599.018.218.20.4549.5
5558.8279.0740.0179.67936766.0198916.710266.09.2066.00.1553.3289402883167.018.418.40.4489.2
6658.6281.0770.0156.76221763.0286116.210663.09.4263.00.1445.893298284331.018.618.70.4348.9
7758.1287.0800.0325.87392564.0159915.711064.08.3364.00.1373.3611162729431.018.818.90.4338.7
8857.5295.0820.0210.91015663.0114115.211363.06.7363.00.1369.83579626616792.019.019.10.4158.4
9957.3295.0840.0317.17151864.0199014.711658.07.4358.00.1272.5637702589345.019.219.30.4058.1

Last rows

Unnamed: 0Life expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 10-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
2634263473.3135.000.01565.9672178.0074.8082.05.188.00.14192.34975815782.00.10.10.71614.3
2635263573.2137.000.01584.94498982.0074.3084.04.9882.00.14266.55717415328.00.10.10.71814.3
2636263673.0138.000.0163.80295077.0073.8079.04.5177.00.1451.54246214951.00.10.10.71714.4
2637263772.914.000.967.03398182.0073.3084.04.6082.00.1445.18866014577.00.10.10.71214.4
2638263872.8142.001.24471.83076782.0072.7084.04.5982.00.13547.59975014137.00.10.10.70714.4
2639263972.5147.001.084.78380684.0072.1086.04.6784.00.137.8465631364.00.10.10.70314.5
2640264072.6145.001.10569.62550484.0071.5088.05.1386.00.13392.647430135.00.10.10.69814.5
2641264172.5146.002.05568.86928187.007.8088.05.8287.00.12932.31588312357.00.10.10.69814.5
2642264272.4148.001.79503.58819689.007.1089.05.6188.00.12892.52266311689.00.10.10.69514.6
2643264372.315.001.57689.94402289.0069.409.06.5289.00.12594.7499901141.00.10.10.69414.6